def gen_zwyx_type(zwlb):
qs = ZpZwyxByTypeModel.objects
if zwlb:
qs = qs.filter(zwlb=zwlb)
path = f'zwyx_type/{zwlb}.html'
else:
path = 'zwyx_type.html'
# 当月职位月薪与公司性质
df = read_frame(qs.all())
if len(df) > 0:
page = Page()
Grid_chart1 = Timeline(width=1500, height=450, timeline_bottom=0)
Grid_chart2 = Timeline(width=1500, height=450, timeline_bottom=0)
df_group = df.groupby(['year', 'month'])
for name, group in df_group:
month = group['month'].tolist()[0]
year = group['year'].tolist()[0]
df_new = group.groupby('type').apply(get_echarts_all_by_zwyx_value,
'type')
# 薪资
Overlap_chart = Overlap(width=800, height=450)
bar_chart = Bar(f'{zwlb}职位月薪与公司性质')
bar_chart.add('最低薪资',
df_new['type'].tolist(),
df_new['min_zwyx'].tolist(),
is_label_show=True,
is_more_utils=True)
bar_chart.add('最高薪资',
df_new['type'].tolist(),
df_new['max_zwyx'].tolist(),
is_label_show=True,
is_more_utils=True)
line_chart = Line()
line_chart.add("平均薪资",
df_new['type'].tolist(),
[(a + b) / 2
for a, b in zip(df_new['min_zwyx'].tolist(),
df_new['max_zwyx'].tolist())],
is_label_show=True)
Overlap_chart.add(bar_chart)
Overlap_chart.add(line_chart)
Grid_chart1.add(Overlap_chart, f'{year}年{month}月')
# 职位量
chart3 = Pie(f'{zwlb}职位量及招聘人数', width=1500)
chart3.add('职位量'.format(zwlb),
df_new['type'].tolist(),
df_new['count'].tolist(),
is_label_show=True,
is_stack=True,
center=[25, 50])
chart3.add('招聘人数'.format(zwlb),
df_new['type'].tolist(),
df_new['zprs'].tolist(),
is_label_show=True,
is_stack=True,
center=[75, 50])
Grid_chart2.add(chart3, f'{year}年{month}月')
page.add(Grid_chart1)
page.add(Grid_chart2)
page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path)))
def gen_zwyx_dd(zwlb):
qs = ZpZwByAreaModel.objects
if zwlb:
qs = qs.filter(zwlb=zwlb)
path = f'zwyx_dd/{zwlb}.html'
else:
path = 'zwyx_dd.html'
page = Page()
df = read_frame(qs.all())
if len(df) > 0:
df_group = df.groupby(['year', 'month'])
time_line_chart1 = Timeline(width=1500,
height=450,
is_auto_play=False,
timeline_bottom=0)
time_line_chart2 = Timeline(width=1500,
height=450,
is_auto_play=False,
timeline_bottom=0)
for name, group in df_group:
# 地图 平均薪资
month = group['month'].tolist()[0]
year = group['year'].tolist()[0]
df_new = group.groupby('province').apply(
get_echarts_all_by_zwyx_value, 'province')
data = [
(a, (b + c) / 2)
for a, b, c in zip(df_new['province'].tolist(
), df_new['max_zwyx'].tolist(), df_new['min_zwyx'].tolist())
]
chart = Map(f'{zwlb}平均职位月薪与地点', width=1500, height=450)
attr, value = chart.cast(data)
chart.add(f'平均薪资',
attr,
value,
wmaptype='china',
is_label_show=True,
is_visualmap=True,
visual_range=[int(min(value)),
int(max(value))],
visual_pos='right',
visual_top='top')
time_line_chart1.add(chart, f'{year}年{month}月')
# 本月职位量Top20
chart3 = Pie(f'{zwlb}职位量及招聘人数', width=1500)
chart3.add('职位量',
df_new['province'].tolist(),
df_new['count'].tolist(),
center=[25, 50],
is_label_show=True)
chart3.add('招聘人数',
df_new['province'].tolist(),
df_new['zprs'].tolist(),
center=[75, 50],
is_label_show=True)
time_line_chart2.add(chart3, f'{year}年{month}月')
page.add(time_line_chart1)
page.add(time_line_chart2)
page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path)))
def create_charts():
page = Page()
style = Style(width=WIDTH, height=HEIGHT)
df = pd.read_csv('./data_cleaned.csv')
df['CREATE_TIME'] = pd.to_datetime(df['CREATE_TIME'])
df['MONTH'] = 0
months = []
for i in df.CREATE_TIME:
month = i.strftime("%Y-%m")
months.append(month)
df.MONTH = months
chart = Timeline(is_auto_play=True,
timeline_bottom=0,
width=WIDTH,
height=HEIGHT)
for name, c in df.groupby('COMMUNITY_NAME'):
month_count = defaultdict(int)
for month, group in c.groupby('MONTH'):
month_count[month] = len(group)
m_s = sorted(list(month_count.keys()))
m_l = [month_count[i] for i in m_s]
chart_1 = Line("各月份社区事件数", **style.init_style)
chart_1.add(
"事件数",
m_s,
m_l,
mark_point=["max", "min"],
is_more_utils=True,
mark_line=["average"],
is_smooth=True,
)
chart.add(chart_1, name)
page.add(chart)
chart = Timeline(is_auto_play=True,
timeline_bottom=0,
width=WIDTH,
height=HEIGHT)
for month, group in df.groupby('MONTH'):
COMMUNITY_NAME = group.COMMUNITY_NAME.value_counts()
chart_1 = Bar("各月份社区事件数", **style.init_style)
chart_1.add("",
COMMUNITY_NAME.index,
COMMUNITY_NAME.values,
mark_point=["max", "min"],
mark_line=["average"],
is_stack=True)
chart.add(chart_1, month)
page.add(chart)
return page
def test_timeline_pie():
style = Style()
pie_style = style.add(is_label_show=True,
radius=[30, 55],
rosetype="radius")
pie_1 = Pie("2012 年销量比例", "数据纯属虚构")
pie_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_2 = Pie("2013 年销量比例", "数据纯属虚构")
pie_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_3 = Pie("2014 年销量比例", "数据纯属虚构")
pie_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_4 = Pie("2015 年销量比例", "数据纯属虚构")
pie_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
timeline = Timeline(is_auto_play=True,
timeline_bottom=0,
width=1200,
height=600)
timeline.add(pie_1, "2012 年")
timeline.add(pie_2, "2013 年")
timeline.add(pie_3, "2014 年")
timeline.add(pie_4, "2015 年")
timeline.add(pie_5, "2016 年")
assert len(timeline._option.get("baseOption").get("series")) == 0
timeline.render()
def test_timeline_pie():
style = Style()
pie_style = style.add(
is_label_show=True,
radius=[30, 55],
rosetype="radius"
)
pie_1 = Pie("2012 年销量比例", "数据纯属虚构")
pie_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_2 = Pie("2013 年销量比例", "数据纯属虚构")
pie_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_3 = Pie("2014 年销量比例", "数据纯属虚构")
pie_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_4 = Pie("2015 年销量比例", "数据纯属虚构")
pie_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
timeline = Timeline(is_auto_play=True, timeline_bottom=0,
width=1200, height=600)
timeline.add(pie_1, '2012 年')
timeline.add(pie_2, '2013 年')
timeline.add(pie_3, '2014 年')
timeline.add(pie_4, '2015 年')
timeline.add(pie_5, '2016 年')
def ShowNumWithYear(df):
# show bus num in different year at different cities
years = list(set(df['year']))
years.sort()
cities = []
values = []
total_num = 0
geos = [] # store the geo every year
timeline = Timeline(width=1500,height=800,is_auto_play=True, timeline_bottom=-10,timeline_symbol_size=20,\
timeline_play_interval = 800,timeline_left=20,timeline_right=100 , is_timeline_show = False )
for index in range(len(years)):
df_temp = df[df['year'] == years[index]]
cities = cities + list(df_temp['city'])
values = values + list(df_temp['num'])
total_num = sum(values)
geos.append(Geo( str(years[index]) + " , Fist level title" , title_top = "10%" , title_text_size=50 , subtitle = "second level title" , \
subtitle_text_size = 23 , subtitle_color="white", \
title_color="red", title_pos="center", width=1200, height=600, \
background_color='#404a59'))
# type="effectScatter", is_random=True, effect_scale=5 使点具有发散性
geos[index].add("数量", cities, values, type="effectScatter", maptype='china' , is_random=True, effect_scale=3, is_selected = True,is_toolbox_show = True ,is_more_utils =True,\
visual_text_color="#fff", symbol_size=10, is_label_show = True , legend_orient = 'left' ,is_legend_show = False, legend_top = 'bottom' , label_formatter = '{b}' , \
is_visualmap=True, is_roam=True , label_text_color="#00FF00" , is_piecewise=True, label_text_size = 7,visual_range=[1, 300] , \
geo_cities_coords = {'柯桥': [120.443 , 30.0822] ,} , \
pieces=[
{"min":0.1, "max": 500 , "label": "0-500"},
{"min": 500, "max": 1000 , "label": "501-1000"},
{"min": 1001, "max": 2000 , "label": "1001-2000"},
{"min":2001, "max": 5000, "label": "2001-5000"},
{"min":5001, "max": 100000, "label": ">5000"}, ] )
geos[index].show_config()
geos[index].render("数量.html")
# 时间轴定义
timeline.add(geos[index], years[index])
timeline.render('redult.html')
def test_timeline_map():
timeline = Timeline(timeline_bottom=0)
value = [155, 10, 66, 78, 33, 80, 190, 53, 49.6]
attr = ["福建", "山东", "北京", "上海", "甘肃", "新疆", "河南", "广西", "西藏"]
map = Map("Map 结合 VisualMap 示例", width=1200, height=600)
map.add(
"",
attr,
value,
maptype="china",
is_visualmap=True,
visual_text_color="#000",
visual_top="30%",
)
timeline.add(map, "test1")
value = [155, 10, 66, 78, 33]
attr = ["福建", "山东", "北京", "上海", "甘肃"]
map = Map("Map 结合 VisualMap 示例", width=1200, height=600)
map.add(
"",
attr,
value,
maptype="china",
is_visualmap=True,
visual_text_color="#000",
visual_top="30%",
)
timeline.add(map, "test2")
assert len(timeline._option.get("baseOption").get("series")) == 2
timeline.render()
def creatTimeLine(charts, timepoints, speed=500, showTimeLine=True):
tt = Timeline(timeline_play_interval=speed, is_timeline_show=showTimeLine)
cnt = 0
for chart in charts:
tt.add(chart, time_point=timepoints[cnt])
cnt += 1
return tt
def get_pic():
df2 = shops(mob_site)[1]
a = order(df2, order_countJA, order_countHA, '')
sz = order(df2, order_countJ, order_countH, shop_order, 4)[0]
gz = order(df2, order_countJ, order_countH, shop_order, 42)[0]
hz = order(df2, order_countJ, order_countH, shop_order, 53)[0]
hk = order(df2, order_countJ, order_countH, shop_order, 85)[0]
sh = order(df2, order_countJ, order_countH, shop_order, 11)[0]
cs = order(df2, order_countJ, order_countH, shop_order, 205)[0]
fz = order(df2, order_countJ, order_countH, shop_order, 201)[0]
bar_a = out_bar(a, "全国")
bar_sz = out_bar(sz, "深圳")
bar_gz = out_bar(gz, "广州")
bar_hz = out_bar(hz, "杭州")
bar_hk = out_bar(hk, "海口")
bar_sh = out_bar(sh, "上海")
bar_cs = out_bar(cs, "长沙")
bar_fz = out_bar(fz, "福州")
timeline = Timeline(is_auto_play=False, timeline_bottom=0, width=1100)
timeline.add(bar_a, '全国')
timeline.add(bar_sz, '深圳')
timeline.add(bar_gz, '广州')
timeline.add(bar_hz, '杭州')
timeline.add(bar_hk, '海口')
timeline.add(bar_sh, '上海')
timeline.add(bar_cs, '长沙')
timeline.add(bar_fz, '福州')
return timeline
def spot_line(cnt,name):
follow_spot=df[df['channel_id']==cnt]
follow_spot=follow_spot.groupby(by=['spot_id','date'],as_index=False).sum()[['spot_id','date','cost_r']]
spt=follow_spot[follow_spot['date'].apply(lambda x:str(x))==end_time.replace('-', '')].sort_values(by='cost_r').tail(7)['spot_id']
top_spt=follow_spot[follow_spot['spot_id'].isin(spt)] #TOP广告位
lists=[]
for i,j in itertools.product(top_spt['spot_id'].drop_duplicates(),df['date'].drop_duplicates()):
lists.append([i,j])
lists=pd.DataFrame(lists,columns=['spot_id','date'])
top_spt=pd.merge(lists,top_spt,how='left',on=['spot_id','date'])
top_spt=top_spt.fillna(0)
top_spt=pd.merge(top_spt,meta_spot,how='left',left_on='spot_id',right_on='spotid')
top_spt.loc[top_spt['name'].isnull(),'name']=top_spt[top_spt['name'].isnull()]['spot_id']
line = Line(name+'TOP广告位消耗')
for i in top_spt['name'].drop_duplicates():
line.add(i,top_spt[top_spt['name']==i]['date'].apply(lambda x:str(x)),top_spt[top_spt['name']==i]['cost_r'].apply(lambda x:int(x)),legend_pos="84%",mark_point=['max'])
# grid= Grid(width=1000,height=350)
# grid.add(line,grid_right="25%")
# page.add_chart(grid)
''' 涨跌幅TOP广告位 '''
lists=[]
for i,j in itertools.product(follow_spot['spot_id'].drop_duplicates(),df['date'].drop_duplicates()):
lists.append([i,j])
lists=pd.DataFrame(lists,columns=['spot_id','date'])
follow_spt=pd.merge(lists,follow_spot,how='left',on=['spot_id','date'])#笛卡尔积
follow_spt=follow_spt.fillna(0)
diff_spt=follow_spt.groupby(by='spot_id',as_index=False).diff()['cost_r'].rename('rise')
diff_spt=pd.concat([follow_spt,diff_spt],axis=1)
#涨幅TOP 7
spt=diff_spt[diff_spt['date'].apply(lambda x:str(x))==end_time.replace('-', '')].sort_values(by='rise').tail(7)['spot_id']
rise_spt=diff_spt[diff_spt['spot_id'].isin(spt)] #TOP广告位
rise_spt=pd.merge(rise_spt,meta_spot,how='left',left_on='spot_id',right_on='spotid')
rise_spt.loc[rise_spt['name'].isnull(),'name']=rise_spt[rise_spt['name'].isnull()]['spot_id']
line1 = Line(name+'涨幅TOP广告位',width=1000,height=450)
for i in rise_spt['name'].drop_duplicates():
line1.add(i,rise_spt[rise_spt['name']==i]['date'].apply(lambda x:str(x)),rise_spt[rise_spt['name']==i]['cost_r'].apply(lambda x:int(x)),legend_pos="84%")
#跌幅TOP 7
spt=diff_spt[diff_spt['date'].apply(lambda x:str(x))==end_time.replace('-', '')].sort_values(by='rise').head(7)['spot_id']
fall_spt=diff_spt[diff_spt['spot_id'].isin(spt)] #TOP广告位
fall_spt=pd.merge(fall_spt,meta_spot,how='left',left_on='spot_id',right_on='spotid')
fall_spt.loc[fall_spt['name'].isnull(),'name']=fall_spt[fall_spt['name'].isnull()]['spot_id']
line2 = Line(name+'跌幅TOP广告位',width=1000,height=450)
for i in fall_spt['name'].drop_duplicates():
line2.add(i,fall_spt[fall_spt['name']==i]['date'].apply(lambda x:str(x)),fall_spt[fall_spt['name']==i]['cost_r'].apply(lambda x:int(x)),legend_pos="84%")
#画图
grid= Grid(width=900,height=350)
timeline = Timeline(timeline_bottom=0)
timeline.add(line, name+'TOP消耗')
timeline.add(line1, name+'涨幅TOP消耗')
timeline.add(line2, name+'跌幅TOP消耗')
grid.add(timeline)
# grid= Grid(width=1200,height=350)
# grid.add(line,grid_right="55%")
# grid.add(line1,grid_left="55%")
page.add_chart(grid)
def test_timeline_label_color():
attr = ["{}月".format(i) for i in range(1, 7)]
bar = Bar("1 月份数据", "数据纯属虚构")
bar.add(
"bar",
attr,
[randint(10, 50) for _ in range(6)],
label_color=["red", "#213", "black"],
)
line = Line()
line.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_0 = Overlap()
overlap_0.add(bar)
overlap_0.add(line)
bar_1 = Bar("2 月份数据", "数据纯属虚构")
bar_1.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_1 = Line()
line_1.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_1 = Overlap()
overlap_1.add(bar_1)
overlap_1.add(line_1)
timeline = Timeline(timeline_bottom=0)
timeline.add(overlap_0, "1 月")
timeline.add(overlap_1, "2 月")
content = timeline._repr_html_()
assert '"color": [' in content
assert "red" in content
assert "#213" in content
assert "black" in content
def gen_gwzz_word(zwlb):
qs = ZpWordByZwlbModel.objects
if zwlb:
qs = qs.filter(zwlb=zwlb)
path = f'zp_word/{zwlb}.html'
else:
path = 'zp_word.html'
df = read_frame(qs.all())
if len(df) > 0:
page = Page()
Grid_chart1 = Timeline(width=1500, height=800, timeline_bottom=0)
df_group = df.groupby(['year', 'month'])
for name, group in df_group:
month = group['month'].tolist()[0]
year = group['year'].tolist()[0]
df_new = group.groupby('word').apply(get_echarts_all_by_value,
'word')
chart = WordCloud(f'{zwlb}岗位需求词云', width=1500)
shape_list = [
None, 'circle', 'cardioid', 'diamond', 'triangle-forward',
'triangle', 'pentagon', 'star'
]
chart.add("",
df_new['word'].tolist(),
df_new['count'].tolist(),
word_size_range=[30, 100],
rotate_step=66,
shape=shape_list[random.randint(0,
len(shape_list) - 1)])
Grid_chart1.add(chart, f'{year}年{month}月')
page.add(Grid_chart1)
page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path)))
def _initial(self, speed):
timeline = Timeline(width=self.width,
height=self.height,
timeline_play_interval=speed)
index = 0
for bar in self.bars:
timeline.add(bar, time_point=self.timePoints[index])
index += 1
return timeline
def create_charts():
page = Page()
style = Style(width=WIDTH, height=HEIGHT)
df = pd.read_csv('./data_cleaned.csv')
STREET_NAME = df.STREET_NAME.value_counts()
chart = Bar("街道种类", **style.init_style)
chart.add(
"",
STREET_NAME.index,
STREET_NAME.values,
mark_point=["max", "min"],
mark_line=["average"],
is_stack=True,
)
page.add(chart)
chart = Timeline(is_auto_play=True,
timeline_bottom=0,
width=WIDTH,
height=HEIGHT)
for name, c in df.groupby('STREET_NAME'):
EVENT_TYPE_NAME = c.EVENT_TYPE_NAME.value_counts()
chart_1 = Bar("各社区事件类型", **style.init_style)
chart_1.add("",
EVENT_TYPE_NAME.index,
EVENT_TYPE_NAME.values,
mark_point=["max", "min"],
mark_line=["average"],
is_stack=True)
chart.add(chart_1, name)
page.add(chart)
table = pd.pivot_table(df,
index=['STREET_NAME'],
columns=['EVENT_TYPE_NAME'],
values=['SUB_TYPE_NAME'],
aggfunc='count',
fill_value=0,
margins=1)
table = table.ix[:-1, :-1]
comm = list(table.index)
name = [i[1] for i in table]
value = [[i, j, float(table.values[i][j])] for i in range(len(comm))
for j in range(len(name))]
chart = HeatMap("街道与事件热力图", width=WIDTH, height=HEIGHT)
chart.add("",
comm,
name,
value,
is_visualmap=True,
visual_text_color="#000",
visual_orient='horizontal')
page.add(chart)
return page
def show_ruanke_best(begin_year,end_year):
'''
@author: Hong wentao
用于展示每一个学科的学校的排名可视化，使用的是软科最好大学排名的数据
'''
path=data_path+"ruanke_best_subject/"
ruanke_html_path=html_path+"ruanke/"
isExists=os.path.exists(ruanke_html_path)
if not isExists:
os.makedirs(ruanke_html_path)
first_names=os.listdir(path)
for first_name in first_names:
first_data_path=path+first_name+"/"
first_html_path=ruanke_html_path+first_name+"/"
isExists=os.path.exists(first_html_path)
if not isExists:
os.makedirs(first_html_path)
second_names=os.listdir(first_data_path)
for second_name in second_names:
second_data_path=first_data_path+second_name+"/"
second_html_path=first_html_path+second_name+"/"
isExists=os.path.exists(second_html_path)
if not isExists:
os.makedirs(second_html_path)
data=deal_ruanke.every_subject_ruanke(first_name,second_name)
timeline = Timeline(is_auto_play=False, timeline_bottom=0,width=stand_width)
for i in range(begin_year,end_year+1):
bar = Bar(second_name+str(i)+"年大学排名数据")
try:
want_data=data[i]
nan='空白区域说明该学校当年无该数据'
final_data=[]
have_name=[]
before_median=[]
for x in want_data:
have_name.append(x[2])
for name in university_name:
if name not in have_name:
final_data.append("nan")
else:
for x in want_data:
if x[2]==name:
final_data.append(x[0])
before_median.append(x[0])
break;
a=np.array(before_median).astype("int")
median=np.median(a).astype("int")
bar.add("排名 "+nan, university_name,final_data, is_label_show=True,is_stack=True,xaxis_interval=0,xaxis_name_size=15,xaxis_rotate=0,yaxis_name_rotate=0,xaxis_name_pos="end", xaxis_name="学校名",yaxis_name="排名",yaxis_name_gap=10,yaxis_name_pos="end",mark_point=["max","min"],mark_line_raw=[{"yAxis": median,"name":"中位数","lineStyle": {
"color": 'black'
}}])
timeline.add(bar, str(i)+'年')
timeline.render(second_html_path+second_name+".html")
except Exception:
print(first_name+" "+second_name+str(i)+"没有")
continue
timeline.render(second_html_path+second_name+".html")
def test_null_in_timeline_options():
bar_1 = Bar("2012 年销量", "数据纯属虚构")
bar_1.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1._option["series"][0]["symbol"] = NULL
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar_1, "2012 年")
assert isinstance(timeline._option["options"][0]["series"][0]["symbol"],
JsValue)
def gauge_timeline(data_array):
chart1 = Timeline(**timeline_Style)
array_gauge = []
for i in range(0, 13):
array_gauge.append(data_array[i]['heat'].sum())
maxgauge = max(array_gauge)
array_gauge = soomthByStage(array_gauge, 5)
for i in range(0, 60):
gauge = Gauge("观看人数趋势图 ", title_pos='center')
gauge.add("", "占比", int(array_gauge[i] / maxgauge * 100))
chart1.add(gauge, String_filename_1[i])
return chart1
def show_map_school(begin_year, end_year):
'''
用地图展示各学校的排名，这样可以观察地域与学校排名的关系
'''
datas = deal_ruanke.ranking_ruanke(begin_year, end_year)
university_name = (list)(datas.keys())
ranking_datas = (list)(datas.values())
timeline = Timeline(is_auto_play=False,
timeline_bottom=0,
width=stand_width,
height=500)
for i in range(begin_year, end_year + 1):
data = []
dict = {}
nan = ''
for j in range(0, len(university_name)):
line = []
line.append(university_name[j])
if (ranking_datas[j][i - begin_year] == "nan"):
continue
line.append(ranking_datas[j][i - begin_year])
line_tuple = (tuple)(line)
data.append(line_tuple)
dict[university_name[j]] = ranking_datas[j][i - begin_year]
if (i == 2016):
nan = "(云南大学，青海大学，西藏大学未纳入排名)"
geo = Geo(str(i) + "年度全国大学排名" + nan,
"全国大学排名",
title_color="#000",
title_pos="center",
width=1600,
height=400,
background_color='#DCDCDC')
attr, value = geo.cast(data)
geo.add("",
attr,
value,
visual_range=[0, 600],
maptype='china',
visual_text_color="#000",
symbol_size=20,
is_visualmap=True,
is_roam=False,
visual_top="center",
geo_normal_color="#404a59",
visual_range_text=["high", "low"],
visual_range_color=['#50a3ba', '#faef61', '#d94e5d'],
label_formatter="{c0}",
tooltip_formatter="{b}: [经度(E)，纬度(N)，排名] {c}名")
geo
timeline.add(geo, str(i) + '年')
return timeline
def time_line():
timeline = Timeline(page_title="Time Line")
stack_bar, line = StackBar.stack_bar()
tree_chart = TreeChart.tree_chart()
stack_bar_chart = StackBarChart.stack_bar_chart2()
stack_line = StackChart.starck_line_chart()
timeline.add(stack_line, time_point=7)
timeline.add(stack_bar_chart, time_point=8)
timeline.add(tree_chart, time_point=6)
timeline.add(stack_bar,time_point=2)
timeline.add(line, time_point=3)
timeline.render("timeline.html")
def geographical_location_GDP_ratio_distribution():
data = pd.read_excel("./data/car_sales_data.xlsx", sheet_name=u"人均GDP", encoding='utf-8')
data.columns = [str(col).split('-')[0] for col in data.columns]
float_cols = [str(year) for year in range(1998, 2018)]
not_replace = [
u'池州市', u'宣城市', u'眉山市', u'达州市', u'庆阳市',
u'广安市', u'贺州市', u'来宾市', u'崇左市', u'临沧市',
u'固原市', u'中卫市', u'丽江市'
]
data['city_name'] = data['city_name'].apply(lambda xx: xx.replace(u'市', '') if xx not in not_replace else xx)
city_none = [u'黔西南布依族苗族自治州', u'乌兰察布', u'巴音郭楞蒙古自治州（库尔勒）']
data = data[~data['city_name'].isin(city_none)]
data[float_cols] = data[float_cols].fillna(0).applymap(lambda xx: round(xx, 2) if xx else 0)
cols_show = ['city_name'] + float_cols
data = data[cols_show]
data = calc_ratio_percent(data)
data[range(1999, 2018)] = data[range(1999, 2018)].applymap(lambda xx: round(xx * 100, 2))
data.to_excel('./car_sales_visualization/GDP_ratio_distribution.xlsx')
timeline = Timeline(is_auto_play=False, timeline_bottom=1250, width=2480, height=1330)
for year in range(1999, 2018):
dataset = [(city, sales) for city, sales in data[['city_name'] + [year]].values]
geo = Geo(
"%s - Car Sales Num Ratio Distribution" % year,
"",
title_pos="center",
title_color="black",
width=2480,
height=1330,
background_color='#ffffff'
)
attr, value = geo.cast(dataset)
geo.add(
"",
attr,
value,
type="effectScatter",
is_visualmap=True,
maptype='china',
visual_range=[-100.0, 100.0],
visual_text_color="black",
effect_scale=5,
symbol_size=5
)
timeline.add(geo, year)
timeline.render('./car_sales_visualization/GDP_ratio_distribution.html')
def test_timeline_bar_line():
attr = ["{}月".format(i) for i in range(1, 7)]
bar = Bar("1 月份数据", "数据纯属虚构")
bar.add("bar", attr, [randint(10, 50) for _ in range(6)])
line = Line()
line.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_0 = Overlap()
overlap_0.add(bar)
overlap_0.add(line)
bar_1 = Bar("2 月份数据", "数据纯属虚构")
bar_1.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_1 = Line()
line_1.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_1 = Overlap()
overlap_1.add(bar_1)
overlap_1.add(line_1)
bar_2 = Bar("3 月份数据", "数据纯属虚构")
bar_2.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_2 = Line()
line_2.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_2 = Overlap()
overlap_2.add(bar_2)
overlap_2.add(line_2)
bar_3 = Bar("4 月份数据", "数据纯属虚构")
bar_3.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_3 = Line()
line_3.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_3 = Overlap()
overlap_3.add(bar_3)
overlap_3.add(line_3)
bar_4 = Bar("5 月份数据", "数据纯属虚构")
bar_4.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_4 = Line()
line_4.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_4 = Overlap()
overlap_4.add(bar_4)
overlap_4.add(line_4)
timeline = Timeline(timeline_bottom=0)
timeline.add(overlap_0, '1 月')
timeline.add(overlap_1, '2 月')
timeline.add(overlap_2, '3 月')
timeline.add(overlap_3, '4 月')
timeline.add(overlap_4, '5 月')
assert len(timeline._option.get("baseOption").get("series")) == 10
timeline.render()
def show_total_ruanke(begin_year, end_year):
'''
1.软科总体排名
@author: Hong wentao
最终的可视化结果呈现出的是2016年至今这14所学校在软科最好大学排名中的排名名次
'''
datas = deal_ruanke.ranking_ruanke(begin_year, end_year)
university_name = (list)(datas.keys())
ranking_datas = (list)(datas.values())
timeline = Timeline(is_auto_play=False,
timeline_bottom=0,
width=stand_width)
for i in range(0, end_year - begin_year + 1):
bar = Bar("软科" + str(2016 + i) + "年大学排名数据")
want_data = []
nan = ''
before_median = []
for x in ranking_datas:
want_data.append(x[i])
if x[i] == "nan":
nan = "（如果某学校当年无数据，则该区域为为空白）"
else:
before_median.append(x[i])
a = np.array(before_median).astype("int")
median = np.median(a).astype("int")
bar.add("排名 " + nan,
university_name,
want_data,
is_label_show=True,
is_stack=True,
xaxis_interval=0,
xaxis_name_size=15,
xaxis_rotate=0,
yaxis_name_rotate=0,
xaxis_name_pos="end",
xaxis_name="学校名",
yaxis_name="排名",
yaxis_name_gap=10,
yaxis_name_pos="end",
mark_point=["max", "min"],
mark_line_raw=[{
"yAxis": median,
"name": "中位数",
"lineStyle": {
"color": 'black'
}
}])
timeline.add(bar, str(2016 + i) + ' 年')
return timeline
def test_timeline_bar_line():
attr = ["{}月".format(i) for i in range(1, 7)]
bar = Bar("1 月份数据", "数据纯属虚构")
bar.add("bar", attr, [randint(10, 50) for _ in range(6)])
line = Line()
line.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_0 = Overlap()
overlap_0.add(bar)
overlap_0.add(line)
bar_1 = Bar("2 月份数据", "数据纯属虚构")
bar_1.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_1 = Line()
line_1.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_1 = Overlap()
overlap_1.add(bar_1)
overlap_1.add(line_1)
bar_2 = Bar("3 月份数据", "数据纯属虚构")
bar_2.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_2 = Line()
line_2.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_2 = Overlap()
overlap_2.add(bar_2)
overlap_2.add(line_2)
bar_3 = Bar("4 月份数据", "数据纯属虚构")
bar_3.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_3 = Line()
line_3.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_3 = Overlap()
overlap_3.add(bar_3)
overlap_3.add(line_3)
bar_4 = Bar("5 月份数据", "数据纯属虚构")
bar_4.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_4 = Line()
line_4.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_4 = Overlap()
overlap_4.add(bar_4)
overlap_4.add(line_4)
timeline = Timeline(timeline_bottom=0)
timeline.add(overlap_0, "1 月")
timeline.add(overlap_1, "2 月")
timeline.add(overlap_2, "3 月")
timeline.add(overlap_3, "4 月")
timeline.add(overlap_4, "5 月")
timeline.render()
def geographical_location_ratio_distribution():
data = pd.read_csv("./data/car_sales_volume_data.csv", encoding='utf-8')
data = data[data['city'].str.len() > 1]
data = calc_ratio_percent(data)
data[range(1998, 2019)] = data[range(1998, 2019)].applymap(lambda xx: round(xx * 100, 4))
cols_show = ['city'] + range(1998, 2019)
data = data[cols_show]
city_none = [
u'三沙', u'中卫', u'临沧', u'丽江',
u'克孜勒苏柯尔克孜自治州', u'其它',
u'固原', u'新疆自治区直辖', u'普洱',
u'河南省省直辖', u'海南省省直辖', u'湖北省省直辖'
]
data = data[~data['city'].isin(city_none)]
data.to_excel('./car_sales_visualization/car_sales_num_ratio.xlsx')
timeline = Timeline(is_auto_play=False, timeline_bottom=1250, width=2480, height=1330)
for year in range(1998, 2019):
dataset = [(city, sales) for city, sales in data[['city'] + [year]].values]
geo = Geo(
"%s - Car Sales Num Ratio Distribution" % year,
"",
title_pos="center",
title_color="black",
width=2480,
height=1330,
background_color='#ffffff'
)
attr, value = geo.cast(dataset)
geo.add(
"",
attr,
value,
type="effectScatter",
is_visualmap=True,
maptype='china',
visual_range=[-100, 100],
visual_text_color="black",
effect_scale=5,
symbol_size=5
)
timeline.add(geo, year)
timeline.render('./car_sales_visualization/car_sales_num_ratio.html')
def sale():
from pyecharts import Bar, Timeline
from random import randint
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
bar_1 = Bar("2012 年销量", "数据纯属虚构")
bar_1.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_1.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_1.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_1.add("冬季", attr, [randint(10, 100) for _ in range(6)])
bar_2 = Bar("2013 年销量", "数据纯属虚构")
bar_2.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_2.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_2.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_2.add("冬季", attr, [randint(10, 100) for _ in range(6)])
bar_3 = Bar("2014 年销量", "数据纯属虚构")
bar_3.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_3.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_3.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_3.add("冬季", attr, [randint(10, 100) for _ in range(6)])
bar_4 = Bar("2015 年销量", "数据纯属虚构")
bar_4.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_4.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_4.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_4.add("冬季", attr, [randint(10, 100) for _ in range(6)])
bar_5 = Bar("2016 年销量", "数据纯属虚构")
bar_5.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_5.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_5.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_5.add("冬季",
attr, [randint(10, 100) for _ in range(6)],
is_legend_show=True)
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar_1, '2012 年')
timeline.add(bar_2, '2013 年')
timeline.add(bar_3, '2014 年')
timeline.add(bar_4, '2015 年')
timeline.add(bar_5, '2016 年')
return template(timeline)
def get_html_project_report(project_name, type=0):
mylogger = common.LogOutput.Log().get_logger()
mylogger.info(
"------------------------------------------------------------------")
mylogger.info("当前运行文件：{}".format(__file__))
try:
project_run_num = get_project_run_num(project_name, type)
# num = [successNum, failedNum, noRunNum]
num = [project_run_num[0], project_run_num[1], project_run_num[2]]
attr = ["成功用例", "失败用例", "不执行用例"]
bar = Bar(project_name, "用例执行结果")
bar.add("", attr, num, is_more_utils=True)
bar.show_config()
pie_1 = Pie(project_name, "用例执行结果")
pie_1.add(project_name,
attr,
num,
is_label_show=True,
radius=[30, 55],
rosetype='radius')
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar, '树形图')
timeline.add(pie_1, '饼形图')
PATH = lambda P: os.path.abspath(
os.path.join(os.path.dirname(__file__), P))
html_dirname = PATH(
"../report") + "\\" + project_name + "\\" + "htmlReport\\"
# 判断是否存在case目录，没有，则创建
if not os.path.exists(html_dirname):
os.mkdir(html_dirname)
mylogger.info("生成用例目录路径：{}".format(html_dirname))
if type == 0:
html_report_name = time.strftime('%Y-%m-%d %H-%M-%S') + '.html'
elif type == 1:
html_report_name = 'settingReport' + time.strftime(
'%Y-%m-%d %H-%M-%S') + '.html'
htmlReportPath = html_dirname + html_report_name
timeline.render(htmlReportPath)
return 'success'
except TypeError as e:
return 'caseEmpty', e
def flush_visual_data():
while True:
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
cpudata = cpu(attr)
memdata = mem(attr)
diskdata = disk(attr)
timeline.add(cpudata, 'CPU')
timeline.add(memdata, '内存')
timeline.add(diskdata, '磁盘')
context = dict(
visual_sysinfo=timeline.render_embed(),
host=DEFAULT_HOST,
script_list=timeline.get_js_dependencies(),
)
context_json = json.dumps(context)
with open('temp_json_info', 'wb') as temp_file:
temp_file.write(context_json)
time.sleep(600)
def geographical_location_amount_distribution():
data = pd.read_csv("./data/car_sales_volume_data.csv", encoding='utf-8')
data = data[data['city'].str.len() > 1]
city_none = [
u'三沙', u'中卫', u'临沧', u'丽江',
u'克孜勒苏柯尔克孜自治州', u'其它',
u'固原', u'新疆自治区直辖', u'普洱',
u'河南省省直辖', u'海南省省直辖', u'湖北省省直辖'
]
data = data[~data['city'].isin(city_none)]
int_cols = [str(col) for col in range(1998, 2019)]
data[int_cols] = data[int_cols].applymap(lambda xx: float(xx))
timeline = Timeline(is_auto_play=False, timeline_bottom=1250, width=2480, height=1330)
for year in range(1998, 2019):
dataset = [(city, sales) for city, sales in data[('city %s' % year).split()].values]
print dataset
geo = Geo(
"%s - Car Sales Num Amount Distribution" % year,
"",
title_pos="center",
title_color="black",
width=2700,
height=1340,
background_color='#ffffff'
)
attr, value = geo.cast(dataset)
geo.add(
"",
attr,
value,
maptype='china',
visual_range=[0, 300000],
visual_text_color="black",
type="heatmap",
is_visualmap=True,
effect_scale=5,
symbol_size=5,
)
timeline.add(geo, year)
timeline.render('./car_sales_visualization/car_sales_num_amount.html')
def create_charts():
page = Page()
style = Style(width=WIDTH, height=HEIGHT)
df = pd.read_csv('./data_cleaned.csv')
df['CREATE_TIME'] = pd.to_datetime(df['CREATE_TIME'])
df['MONTH'] = 0
months = []
for i in df.CREATE_TIME:
month = i.strftime("%Y-%m")
months.append(month)
df.MONTH = months
df['DISPOSE_UNIT_NAME_DIST'] = 0
df['DISPOSE_UNIT_NAME_DIST'] = df['DISPOSE_UNIT_NAME'].apply(deletekh)
DISPOSE_UNIT_NAME_DIST = df.DISPOSE_UNIT_NAME_DIST.value_counts()
chart = Bar("处理部门", **style.init_style)
chart.add("",
DISPOSE_UNIT_NAME_DIST.index,
DISPOSE_UNIT_NAME_DIST.values,
mark_point=["max", "min"],
is_datazoom_show=True,
datazoom_range=[0, 30],
mark_line=["average"],
is_stack=True)
page.add(chart)
chart = Timeline(is_auto_play=True,
timeline_bottom=0,
width=WIDTH,
height=HEIGHT)
for month, group in df.groupby('MONTH'):
DISPOSE_UNIT_NAME = group.DISPOSE_UNIT_NAME_DIST.value_counts()
chart_1 = Bar("处理部门事件数", **style.init_style)
chart_1.add("",
DISPOSE_UNIT_NAME.index,
DISPOSE_UNIT_NAME.values,
mark_point=["max", "min"],
mark_line=["average"],
is_stack=True)
chart.add(chart_1, month)
page.add(chart)
return page
def test_timeline_bar():
bar_1 = Bar("2012 年销量", "数据纯属虚构")
bar_1.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2 = Bar("2013 年销量", "数据纯属虚构")
bar_2.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3 = Bar("2014 年销量", "数据纯属虚构")
bar_3.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4 = Bar("2015 年销量", "数据纯属虚构")
bar_4.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5 = Bar("2016 年销量", "数据纯属虚构")
bar_5.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add(
"冬季",
CLOTHES,
[randint(10, 100) for _ in range(6)],
is_legend_show=True,
)
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar_1, "2012 年")
timeline.add(bar_2, "2013 年")
timeline.add(bar_3, "2014 年")
timeline.add(bar_4, "2015 年")
timeline.add(bar_5, "2016 年")
assert len(timeline._option.get("baseOption").get("series")) == 0
timeline.render()